Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Dose, Infection, and Disease Outcomes for Coronavirus Disease 2019 (COVID-19): A Review
Bibliographic record
Abstract
The relationship between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) dose, infection, and coronavirus disease 2019 (COVID-19) outcomes remains poorly understood. This review summarizes the existing literature regarding this issue, identifies gaps in current knowledge, and suggests opportunities for future research. In humans, host characteristics, including age, sex, comorbidities, smoking, and pregnancy, are associated with severe COVID-19. Similarly, in animals, host factors are strong determinants of disease severity, although most animal infection models manifest clinically with mild to moderate respiratory disease. The influence of variants of concern as it relates to infectious dose, consequence of overall pathogenicity, and disease outcome in dose-response remains unknown. Epidemiologic data suggest a dose-response relationship for infection contrasting with limited and inconsistent surrogate-based evidence between dose and disease severity. Recommendations include the design of future infection studies in animal models to investigate inoculating dose on outcomes and the use of better proxies for dose in human epidemiology studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".